Establishment of a nomogram model for predicting Parkinson's disease severity based on neutrophil to high-density lipoprotein cholesterol ratio

SANG Xuelian; ZHAI Zhiyuan; HAO Sijia; CHEN Cheng; LU Jun; WANG Guoqing; CAO Xiangyang; ZHENG Jinlong

doi:10.12467/j.issn.2096-3882.20240206

Journal of Xuzhou Medical University > 2024 > 44(10): 717-724. > DOI: 10.12467/j.issn.2096-3882.20240206

SANG Xuelian, ZHAI Zhiyuan, HAO Sijia, CHEN Cheng, LU Jun, WANG Guoqing, CAO Xiangyang, ZHENG Jinlong. Establishment of a nomogram model for predicting Parkinson's disease severity based on neutrophil to high-density lipoprotein cholesterol ratio[J]. Journal of Xuzhou Medical University, 2024, 44(10): 717-724. DOI: 10.12467/j.issn.2096-3882.20240206

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Establishment of a nomogram model for predicting Parkinson's disease severity based on neutrophil to high-density lipoprotein cholesterol ratio

1. Department of Neurology, Huai'an Clinical College, Xuzhou Medical University, Huai'an, Jiangsu 223001, China;
2. Department of Neurology, Pudong Hospital, Fudan University, Shanghai 200120;
3. Department of Neurology, the Second People's Hospital of Huai'an, Huai'an, Jiangsu 223001

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Received Date: March 18, 2024
Revised Date: September 05, 2024

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Abstract

Objective To explore the relationship between the neutrophil to high-density lipoprotein cholesterol ratio (NHR), monocyte to high-density lipoprotein cholesterol ratio (MHR), and the severity of Parkinson's disease (PD), and to construct a nomogram model for predicting PD severity. Methods A total of 164 patients with primary PD, who were admitted to Huai'an First People's Hospital from January 2017 to June 2023 were selected and set as a PD group. Meanwhile, 161 healthy individuals undergoing routine physical examination were selected as a control group. According to Hoehn-Yahr stages, patients in the PD group was further divided into an early-stage group and a mid-to-late-stage group. Their demographic data, blood routine examination, and blood lipid were collected to calculate NHR and MHR for clinical comparison. Correlation analysis was used to determine the relationship between PD severity and clinical parameters. Logistic regression was employed to assess the independent factors affecting PD severity, and a nomogram model was developed to predict PD severity. Results NHR in the PD group was significantly higher than that in the control group (P<0.05). The mid-to-late-stage PD group showed significantly higher NHR and MHR than the early-stage group (P<0.05). Multivariate logistic regression analysis indicated that NHR, the Unified Parkinson's Disease Rating Scale PartⅢ (UPDRS Ⅲ) score, and disease duration were risk factors for PD severity (P<0.05). Accordingly, a nomogram model was constructed to predict PD severity. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.948 (95% CI: 0.915-0.981, P<0.01), with a specificity of 0.947 and a sensitivity of 0.843. The Hosmer-Lemeshow goodness-of-fit test yielded a χ² value of 6.935 (P>0.05), indicating that the nomogram model has good calibration. The calibration curve showed that the model has a high prediction accuracy, and the decision curve indicated that the model has good clinical benefits. Conclusions The nomogram model based on NHR, UPDRS Ⅲ score, and disease duration can effectively predict the severity of PD, demonstrating good clinical utility.

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